Modelling prevalent cardiovascular disease in an urban Indigenous population.

OBJECTIVE: Studies have highlighted the inequities between the Indigenous and non-Indigenous populations with respect to the burden of cardiovascular disease and prevalence of predisposing risks resulting from historical and ongoing impacts of colonization. The objective of this study was to investigate factors associated with cardiovascular disease (CVD) within and specific to the Indigenous peoples living in Toronto, Ontario, and to evaluate the reliability and validity of the resulting model in a similar population. METHODS: The Our Health Counts Toronto study measured the baseline health of Indigenous community members living in Toronto, Canada, using respondent-driven sampling. An iterative approach, valuing information from the literature, clinical insight and Indigenous lived experiences, as well as statistical measures was used to evaluate candidate predictors of CVD (self-reported experience of discrimination, ethnic identity, health conditions, income, education, age, gender and body size) prior to multivariable modelling. The resulting model was then validated using a distinct, geographically similar sample of Indigenous people living in Hamilton, Ontario, Canada. RESULTS: The multivariable model of risk factors associated with prevalent CVD included age, diabetes, hypertension, body mass index and exposure to discrimination. The combined presence of diabetes and hypertension was associated with a greater risk of CVD relative to those with either condition and was the strongest predictor of CVD. Those who reported previous experiences of discrimination were also more likely to have CVD. Further study is needed to determine the effect of body size on risk of CVD in the urban Indigenous population. The final model had good discriminative ability and adequate calibration when applied to the Hamilton sample. CONCLUSION: Our modelling identified hypertension, diabetes and exposure to discrimination as factors associated with cardiovascular disease. Discrimination is a modifiable exposure that must be addressed to improve cardiovascular health among Indigenous populations.

RéSUMé: OBJECTIF: Des études ont souligné les iniquités entre les populations autochtones et non autochtones en ce qui a trait au fardeau des maladies cardiovasculaires et à la prévalence des risques prédisposants qui résultent des impacts historiques et continus de la colonisation. Nous avons voulu étudier les facteurs associés aux maladies cardiovasculaires (MCV) au sein des populations autochtones vivant à Toronto (Ontario) et spécifiques à ces populations, puis évaluer la fiabilité et la validité du modèle ainsi obtenu dans une population semblable. MéTHODE: L'étude Our Health Counts Toronto a mesuré l'état de santé de départ de membres de communautés autochtones vivant à Toronto, au Canada, à l'aide d'un échantillonnage en fonction des répondants. Une approche itérative, valorisant à la fois les données d'études scientifiques, l'expérience clinique, le vécu de personnes autochtones et les mesures statistiques, a été employée pour évaluer les candidats prédicteurs des MCV (expérience autodéclarée de discrimination, identité ethnique, affections médicales, revenu, instruction, âge, genre et taille) avant la modélisation multivariée. Le modèle ainsi obtenu a ensuite été validé à l'aide d'un échantillon distinct, mais géographiquement similaire, de personnes autochtones vivant à Hamilton (Ontario), au Canada. RéSULTATS: Le modèle multivarié des facteurs de risque associés aux MCV prévalentes incluait l'âge, le diabète, l'hypertension artérielle, l'indice de masse corporelle et l'exposition à la discrimination. La présence combinée du diabète et de l'hypertension artérielle était associée à un risque accru de MCV comparativement à l'une ou l'autre de ces deux affections médicales; c'était aussi la plus forte variable prédictive des MCV. Les personnes ayant déclaré des expériences passées de discrimination étaient aussi plus susceptibles d'être atteintes de MCV. D'autres études sont nécessaires pour déterminer l'effet de la taille sur le risque de MCV dans la population autochtone urbaine. Le modèle final avait un bon pouvoir discriminant et une calibration adéquate lorsqu'il a été appliqué à l'échantillon de Hamilton. CONCLUSION: Notre modélisation a cerné l'hypertension artérielle, le diabète et l'exposition à la discrimination comme facteurs associés aux maladies cardiovasculaires. La discrimination est un risque modifiable qui doit être abordé pour améliorer la santé cardiovasculaire au sein des populations autochtones.

Population-Level Impact and Cost-effectiveness of Continuous Glucose Monitoring and Intermittently Scanned Continuous Glucose Monitoring Technologies for Adults With Type 1 Diabetes in Canada: A Modeling Study.

OBJECTIVE: Maintaining healthy glucose levels is critical for the management of type 1 diabetes (T1D), but the most efficacious and cost-effective approach (capillary self-monitoring of blood glucose [SMBG] or continuous [CGM] or intermittently scanned [isCGM] glucose monitoring) is not clear. We modeled the population-level impact of these three glucose monitoring systems on diabetes-related complications, mortality, and cost-effectiveness in adults with T1D in Canada. RESEARCH DESIGN AND METHODS: We used a Markov cost-effectiveness model based on nine complication states for adults aged 18-64 years with T1D. We performed the cost-effectiveness analysis from a single-payer health care system perspective over a 20-year horizon, assuming a willingness-to-pay threshold of CAD 50,000 per quality-adjusted life-year (QALY). Primary outcomes were the number of complications and deaths and the incremental cost-effectiveness ratio (ICER) of CGM and isCGM relative to SMBG. RESULTS: An initial cohort of 180,000 with baseline HbA1c of 8.1% was used to represent all Canadians aged 18-64 years with T1D. Universal SMBG use was associated with ∼11,200 people (6.2%) living without complications and ∼89,400 (49.7%) deaths after 20 years. Universal CGM use was associated with an additional ∼7,400 (4.1%) people living complications free and ∼11,500 (6.4%) fewer deaths compared with SMBG, while universal isCGM use was associated with ∼3,400 (1.9%) more people living complications free and ∼4,600 (2.6%) fewer deaths. Relative to SMBG, CGM and isCGM had ICERs of CAD 35,017/QALY and 17,488/QALY, respectively. CONCLUSIONS: Universal use of CGM or isCGM in the Canadian T1D population is anticipated to reduce diabetes-related complications and mortality at an acceptable cost-effectiveness threshold.

Construct validity and the validity of replication studies: A systematic review.

Currently, there is little guidance for navigating measurement challenges that threaten construct validity in replication research. To identify common challenges and ultimately strengthen replication research, we conducted a systematic review of the measures used in the 100 original and replication studies from the Reproducibility Project: Psychology (Open Science Collaboration, 2015). Results indicate that it was common for scales used in the original studies to have little or no validity evidence. Our systematic review demonstrates and corroborates evidence that issues of construct validity are sorely neglected in original and replicated research. We identify four measurement challenges replicators are likely to face: a lack of essential measurement information, a lack of validity evidence, measurement differences, and translation. Next, we offer solutions for addressing these challenges that will improve measurement practices in original and replication research. Finally, we close with a discussion of the need to develop measurement methodologies for the next generation of replication research. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Examining adolescent daughters' and their parents' academic-gender stereotypes: Predicting academic attitudes, ability, and STEM intentions.

INTRODUCTION: Women continue to be underrepresented in Science, Technology, Engineering, and Mathematics (STEM) and research suggests that academic-gender stereotypes can be a contributing factor. In the present research, we examined whether adolescent daughters' and their parents' gender stereotypes about math and liberal arts would predict the academic orientation of daughters at a critical time of career related decision-making. METHODS: Participants included girls in late adolescence (N = 185, Mage = 17) and at least one parent (N = 230, Mage = 49), resulting in 147 mother-daughter dyads and 83 father-daughter dyads. Implicit academic-gender stereotypes were measured using an Implicit Association Test (IAT) and explicit stereotypes, academic attitudes, academic ability, and daughters' intentions to pursue a degree in STEM were measured using self-reports. RESULTS: Neither mothers' nor fathers' implicit or explicit academic-gender stereotypes predicted adolescent daughters' implicit stereotypes; however, fathers' explicit stereotypes predicted daughters' explicit stereotypes. In addition, daughters' academic orientation, a latent variable composed of adolescent girls' academic attitudes, academic ability, and intentions to pursue a degree in STEM, was predicted by daughters' own implicit and explicit stereotypes. This was the case for relative orientation toward math versus liberal arts, as well as math (but not liberal arts) orientation. CONCLUSIONS: These findings suggest the importance of challenging academic-gender stereotypes during adolescence and suggest that at this stage in development, mothers' and fathers' academic stereotypes might have limited relation to daughters' own implicit associations with academic domains.

How to Address Non-normality: A Taxonomy of Approaches, Reviewed, and Illustrated.

The linear model often serves as a starting point for applying statistics in psychology. Often, formal training beyond the linear model is limited, creating a potential pedagogical gap because of the pervasiveness of data non-normality. We reviewed 61 recently published undergraduate and graduate textbooks on introductory statistics and the linear model, focusing on their treatment of non-normality. This review identified at least eight distinct methods suggested to address non-normality, which we organize into a new taxonomy according to whether the approach: (a) remains within the linear model, (b) changes the data, and (c) treats normality as informative or as a nuisance. Because textbook coverage of these methods was often cursory, and methodological papers introducing these approaches are usually inaccessible to non-statisticians, this review is designed to be the happy medium. We provide a relatively non-technical review of advanced methods which can address non-normality (and heteroscedasticity), thereby serving a starting point to promote best practice in the application of the linear model. We also present three empirical examples to highlight distinctions between these methods' motivations and results. The paper also reviews the current state of methodological research in addressing non-normality within the linear modeling framework. It is anticipated that our taxonomy will provide a useful overview and starting place for researchers interested in extending their knowledge in approaches developed to address non-normality from the perspective of the linear model.